Onboard chargers play a crucial role in electric and plug-in hybrid vehicles. Operating in switching mode, these chargers generate significant electromagnetic interference (EMI), which can propagate to the power grid. As a result, compliance with relevant EMI standards is essential. Conventional onboard charger designs include single-stage and two-stage topologies. This paper develops EMI models for both configurations. For single-stage chargers, a dual active bridge (DAB) charger is analyzed. For two-stage chargers, two configurations are examined: one integrating a totem-pole power factor correction (PFC) converter with a DAB DC/DC converter, and another combining a totem-pole PFC converter with a CLC DC/DC converter. EMI models for these topologies will be developed. Their EMI will be evaluated through simulations and comparative analysis. Based on the findings, EMI mitigation strategies will be proposed.

Graph matching refers to establishing correspondence between two sets of point while keeping consistency between their edge sets. Recent works in learning-based graph matching have attempted to solve the problem either by linear assignment, which transfers local structure information into node embedding at individual graphs, or by quadratic assignment through vertex classification over their association graph. However, the former embedding-based pipeline methods often neglect second-order edge similarity, leading to decreased accuracy; while the latter quadratic assignment solvers consume significant memory due to huge computation on the association graph. To addressthese issues, our key idea is to integrate a factorized embedding module to efficiently propogate information over the association graph. To this end, we propose a novel factorized embedding-based network, namely FEGM, which takes into account the secondorder edge similarity, as well as a factorization model of GCN network, so that we extend the embedding-based pipeline for learning the Lawler’s QAP while reducing memory consumption. Experimental results show that FEGM achieves a competitive matching accuracy while being superior in time and space efficiency.

This paper proposes a technical approach to calculate the AC power loss of transformer windings for the currents with arbitrary shapes and phase-shifts. AC winding power loss formulas are derived based on vector form, and they are important for analyzing the power loss due to skin and proximity effects. Based on the proposed approach, guidelines to optimize transformer winding structures were proposed and applied to a transformer in an active-clamp Flyback power converter. The auxiliary, CM EMI cancellation, and shielding windings commonly used in power products are all taken into consideration in the guidelines. Simulations and experiments were conducted to verify the developed theory and techniques.

Robust and accurate object association is essential for precise 3D object landmark inference in semantic Simultaneous Localization and Mapping (SLAM), and yet remains challenging due to the detection deficiency caused by high miss detection rate, false alarm, occlusion and limited field-of-view, etc. The 2D location of an object is a crucial complementary cue to the appearance feature, especially in the case of associating objects across frames under large viewpoint changes. However, motion model or trajectory pattern based methods struggle to infer object motion reliably with a moving camera. In this paper, by exploiting the local projective warping consistency, a local homography based 2D motion inference method is proposed to sequentially estimate the object location along with uncertainty. By integrating the deep appearance feature and semantic information, an object association method, named HOA, which is robust to detection deficiency is proposed. Experimental evaluations suggest that the proposed motion prediction method is capable of maintaining a low cumulative error over a long duration, which enhances the object association performance in both accuracy and robustness. Note to Practitioners—This work aims to consistently associate 2D detection boxes corresponding to the same 3D object across images. In tasks of landmark-based navigation, collision avoidance, grasping and manipulation, objects in the task space are commonly simplified into 3D enveloping surfaces (e.g. cuboid or ellipsoid) by using 2D object detection boxes from multiple image views, and accurate data association is a prerequisite for precise enveloping surface reconstruction. This problem remains challenging considering the imperfect object detections, the appearance similarity of objects and the unpredictable trajectory of the moving camera. This work proposes a long-term reliable 2D location prediction algorithm that is capable of handling the complex motion of the target. Along with the appearance feature extracted by a retrain-free deep learning based model, this work proposes an object association method that can simultaneously deal with multiple objects with unknown object categories under the moving camera scenario.

Qi standard, widely used in consumer electronics wireless charging, requires an information exchange between the charger and the charged device. The Amplitude Shift Keying (ASK) technique employed for data transmission from the charged device to the charger is susceptible to disturbances due to load variations or noises. The conventional approach to addressing this issue involves introducing extra modulation circuits to dynamically adjust the modulation depth in response to interference conditions. This paper introduces an innovative control strategy for the synchronous rectifier in the charged device, which doubles the ASK modulation depth. With this technology, the ASK communication can achieve more flexibility in modulating depth, potentially leading to the removal of redundant modulation circuits, reduction of overall system costs, size, complexity, and enhancement of system reliability.

Recent works on semantic Simultaneous Localization and Mapping (SLAM) utilizing object landmarks have shown superiority in terms of robustness and accuracy in tracking and localization. 3D object landmarks represented by a cubic or quadric surface are inferred from 2D object bounding boxes which are typically captured from multiple views by an object detector. Nevertheless, bounding box noises and small camera baseline may lead to an inaccurate 3D object landmark inference. Inspired by the dual quadric enveloping property, in this work, we introduce the horizontal support assumption to constrain rotation w.r.t. roll and pitch for a quadric representation. As the result, we reduce the number of quadric parameters and narrow down the solution space, and ultimately produce a relatively accurate inference. Extensive experimental evaluations under both simulated and real scenarios are conducted in this paper. Quantitative results demonstrate that our approach outperforms the state-of-the-art.